Triple

T3150446
Position Surface form Disambiguated ID Type / Status
Subject Gilda Radner E65863 entity
Predicate spouse P13 FINISHED
Object Gene Wilder E238650 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Gene Wilder | Statement: [Gilda Radner, spouse, Gene Wilder]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gene Wilder
Context triple: [Gilda Radner, spouse, Gene Wilder]
  • A. Gene Wilder chosen
    Gene Wilder was an American comic actor and writer best known for his roles in films such as Willy Wonka & the Chocolate Factory, Young Frankenstein, and Blazing Saddles.
  • B. Walter Matthau
    Walter Matthau was an American actor renowned for his gruff charm and comedic roles in films such as "The Odd Couple" and "The Bad News Bears."
  • C. Charles Grodin
    Charles Grodin was an American actor, comedian, and writer known for his deadpan delivery in films such as "The Heartbreak Kid," "Midnight Run," and the "Beethoven" series.
  • D. Charles Matthau
    Charles Matthau is an American film and television director and producer, and the son of actor Walter Matthau.
  • E. Jonathan Winters
    Jonathan Winters was an influential American comedian and actor renowned for his improvisational genius, character work, and pioneering impact on modern stand-up and sketch comedy.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69ad8584485081909ed529e890cadc4a completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada5bf902c8190a490fa55e2dcecc0 completed March 8, 2026, 4:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69b224f94f3881909a277c45c9add0f5 completed March 12, 2026, 2:29 a.m.
Created at: March 8, 2026, 3:05 p.m.